To interpret birth and death times of dimension-1 topological features as information about (inter)stripe width and number in Step 3 [1]}, we rely on a birth-time cutoff factor \(B_m\)  and persistence threshold \(P_m\) , where \(m \in \lbrace \text{ABM},\text{CA}\rbrace \)  denotes the potential for different values for the two models [2]}, [3]}. For values \(B_m = 1, 1.25, 1.5, ..., 5.5,5.75, 6\)  and \(P_m =100,120,140,...,460,480,500\)  \(\mu \) m in Eqns. (REF )–(REF ), we quantify 100 ABM and CA patterns, cropped and cleaned as described in §REF  and §REF , under different choices of \(B_m\)  and \(P_m\) . In Figure REF , we show the effects of these hyper-parameters on the percentage of patterns that our methods identify as having broken stripes or interstripes. (We find that stripe and interstripe width is less sensitive to \(B_m\)  and \(P_m\) ; see Appendix .) The extreme choice of \(B_\text{m}=1\)  requires that loops corresponding to (inter)stripes be born at a scale strictly less than the mean distance between cells, and this intuitively leads to no patterns being identified as having three interstripes and two stripes; as expected, our methods flag 100% of patterns as broken when \(B_m\)  is too low in Figure REF . Similarly, a persistence threshold \(P_m\)  that is too large—e.g., beyond the width of interstripes—means our methods flag all patterns as broken because the number of dimension-1 topological features with large enough persistence is below our target of three.
